Massive multiple-input multiple-output (MIMO) is one of the most favourable technologies to increase throughput in 5G and beyond 5G networks. Having a base station (BS) equipped with a large antenna array helps to suppress the multiuser interference and to improve the spectral efficiency [1]}. However, in FDD mode, this benefit can be only achieved with an accurate downlink (DL) CSI acquisition at the BS. Therefore, in existing FDD MIMO systems, the mobile terminal (MT) first estimates the DL CSI through a pilot training phase and subsequently reports the estimated DL CSI to the BS. Practical approaches to reduce the feedback overhead include sending the CSI only for a subset of antennas [2]}, and codebook based approaches in which the MT sends back to the BS a precoding matrix indicator. On the other hand, in the recent years, artificial intelligence (AI)-based methods have been successively developed for CSI compression and reconstruction. The first example in this direction is represented by CsiNet [3]}, where the DL CSI is regarded as an image, and an autoencoder based on a convolutional NN is introduced. Building upon the success of CsiNet, the most recent works [4]}, [5]} focus on the design of lightweight NNs that reduce the storage space required at the MT and that achieve better encoding efficiency at the same time. Among the latest advances, a user-centric distributed learning has been proposed in [6]} to improve the feedback accuracy by preventing overfitting.
In this work, instead we consider the setup of [7]}, where an AE NN is trained at the BS in a centralized manner using uplink (UL) CSI data, and subsequently the encoder part is offloaded to all the MTs in the cell such that they can compress and feedback the DL CSI. Notably, the training procedure, which requires a high computational effort, is completely done at the BS and not at the MTs. This result is based on the idea proposed in [8]} where it is shown that a NN trained on UL data can generalize to DL data, especially, when the frequency gap between the UL and DL center frequencies is small compared to the UL center frequency.
Recently, the idea of [8]} has been successfully extended also to “linear” architectures, such as the principal component analysis (PCA)-based AE in [10]}.
Please, note that in the setups of [7]}, [8]}, [10]} it is highly recommended that: i) the number of parameter of the encoder NN is small, since the encoder has to be offloaded to the users; ii) the compressed CSI that has to be fed back to the BS can be encoded with a small amount of bits, reducing in this way the feedback overhead.
